Field
Embodiments of the present disclosure generally relate to apparatus and methods for predicting lamp failure in a thermal processing chamber.
Description of the Related Art
With the on-going pressures of lowering cost, improving quality and reducing variability in the face of larger wafer and smaller feature sizes, the nano-manufacturing industry has begun to embrace a move to augmenting reactive operation with prediction. Predictive capabilities such as predictive maintenance (PdM) are cited by the ITRS as critical technologies to incorporate into production over the next five years, with PdM identified as a key component to reduce unscheduled downtime, maintain high quality, and reduce cost.
Many thermal process chambers use lamps to perform various processes such as film deposition, annealing, dopant activation, rapid thermal oxidation, or silicidation etc. Lamp failure is a critical downtime event type in thermal processes, and can result in costly downtime, loss of yield and possibly wafer scrap. Predicting lamp failure results in a lowering of the associated unscheduled downtime and/or extending uptime.
As lamps change properties over time, including degrading, there is a need to maintain consistent and correct temperature profiles in the process chamber so that acceptable yields are maintained right up until the point at which the equipment is brought down and the lamp is replaced. This is achieved by making “tunings” which adjustments to the equipment settings such as power levels to the lamps and gas flows. These adjustments and adjustment magnitudes are not always recorded or accessible by PdM models. However, these adjustments provide a significant source of noise to the prediction models, lowering their effectiveness (e.g., by increasing false positive or reducing true positive prediction levels).
Accordingly, there is a need for improved methods to reduce the impact of tuning disturbances so that prediction of lamp failure can be performed effectively.